Training and Capacity Building in LMIC for Research in Heart and Lung Diseases: The NHLBI—UnitedHealth Global Health Centers of Excellence Program
Bibliographic record
Abstract
Stemming the tide of noncommunicable diseases (NCDs) worldwide requires a multipronged approach. Although much attention has been paid to disease control measures, there is relatively little consideration of the importance of training the next generation of health-related researchers to play their important role in this global epidemic. The lack of support for early stage investigators in low- and middle-income countries interested in the global NCD field has resulted in inadequate funding opportunities for research, insufficient training in advanced research methodology and data analysis, lack of mentorship in manuscript and grant writing, and meager institutional support for developing, submitting, and administering research applications and awards. To address this unmet need, The National Heart, Lung, and Blood Institute-UnitedHealth Collaborating Centers of Excellence initiative created a Training Subcommittee that coordinated and developed an intensive, mentored health-related research experience for a number of early stage investigators from the 11 Centers of Excellence around the world. We describe the challenges faced by early stage investigators in low- and middle-income countries, the organization and scope of the Training Subcommittee, training activities, early outcomes of the early stage investigators (foreign and domestic) and training materials that have been developed by this program that are available to the public. By investing in the careers of individuals in a supportive global NCD network, we demonstrate the impact that an investment in training individuals from low- and middle-income countries can have on the preferred future of or current efforts to combat NCDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".